Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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... normal or Gaussian probability - density function is important because of its computational simplicity and because it represents a realistic model of many pattern - classification situations . Furthermore , normal distributions ...
... normal or Gaussian probability - density function is important because of its computational simplicity and because it represents a realistic model of many pattern - classification situations . Furthermore , normal distributions ...
Sivu 54
... normal distribution which describes the joint probability density of d components . Patterns selected according to this joint proba- bility distribution will be called multivariate normal patterns or , more simply , normal patterns ...
... normal distribution which describes the joint probability density of d components . Patterns selected according to this joint proba- bility distribution will be called multivariate normal patterns or , more simply , normal patterns ...
Sivu 55
... normal patterns We are now ready to derive the optimum classifier for normal patterns . We shall temporarily assume that for each category i , where i 1 , = R , we know the a priori probability p ( i ) and the particular d - variate ...
... normal patterns We are now ready to derive the optimum classifier for normal patterns . We shall temporarily assume that for each category i , where i 1 , = R , we know the a priori probability p ( i ) and the particular d - variate ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
11 | 30 |
PARAMETRIC TRAINING METHODS | 43 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components Computer consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |